Intelligent learning auxiliary system and method based on NLP and voice processing

Abstract

The embodiment of the invention relates to the technical field of voice interaction, and discloses an intelligent learning auxiliary system and method based on NLP and voice processing, and the intelligent learning auxiliary system based on NLP and voice processing does not need a user to manually input the content during learning, and improves the learning efficiency. According to the method, the required learning content can be quickly obtained only through voice interaction and simple interface operation (such as clicking or touching), so that the learning process is greatly simplified, the learning burden of the user in the learning process is reduced, and the learning efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of voice interaction technology, and in particular to an intelligent learning assistance system and method based on NLP and voice processing. Background Art

[0002] With the rapid development of information technology, the field of education is undergoing profound changes. The traditional education model is gradually shifting towards intelligence and personalization, among which intelligent learning assistance systems based on natural language processing (NLP) and speech processing have become a research hotspot. Such systems aim to provide users with a more efficient and convenient learning experience through advanced artificial intelligence technology.

[0003] In existing intelligent learning assistance systems, users often need to manually select the type of learning resources and learning methods. For example, when users encounter English words or sentences they do not understand, they may need to first enter a query through the keyboard or touch screen, and then select one of the multiple learning methods provided to learn. However, this manual selection method is not only cumbersome to operate, but may also lead to poor learning results due to differences in user personal preferences and current learning status. Summary of the invention

[0004] The main purpose of the present invention is to provide an intelligent learning assistance system and method based on NLP and speech processing, aiming to solve the technical problems in the prior art that the learning assistance system is cumbersome to operate and the auxiliary learning efficiency is low.

[0005] To achieve the above objectives, in a first aspect, an embodiment of the present application provides an intelligent learning assistance system based on NLP and speech processing, including a memory and a processor, the processor including a first processor and a second processor, the memory storing computer-readable instructions, the first processor and / or the second processor being used to execute the computer-readable instructions to implement an intelligent learning assistance method, the method comprising: In response to the user selecting and confirming a target object, the target object includes an English sentence, an English paragraph or an English word in the English text; Generating a current auxiliary learning mode for the target object according to the historical auxiliary learning data and outputting the current auxiliary learning mode in voice, wherein the current auxiliary learning mode includes one of object translation, spoken language playback, and spoken language practice, and the current auxiliary learning mode in voice output is used for the user to confirm or reply; Acquire voice response data of the user to the current auxiliary learning mode of the voice output, and generate a target auxiliary learning mode based on the analysis result of the voice response data; The auxiliary learning content of the target assisted learning method is output for the target object. When the target assisted learning method is object translation, the translation content for the target object is output as text. When the target assisted learning method is oral playback, the oral playback content for the target object is output as voice. When the target assisted learning method is oral practice, the oral practice reminder is output as voice.

[0006] In a possible implementation, the first processor and / or the second processor is used to execute the computer-readable instructions to implement the intelligent learning assistance method, and the method further includes: If no voice response data of the current auxiliary learning mode to the voice output of the user is obtained within a first preset time period, the current auxiliary learning mode is set as the target auxiliary learning mode.

[0007] In a possible implementation, the historical auxiliary learning data includes the cumulative learning time of the user, and the generating of the current auxiliary learning mode for the target object according to the historical auxiliary learning data and voice output of the current auxiliary learning mode includes: Determine the user's learning progress based on the user's cumulative learning time; According to the user's learning progress, query the relationship mapping table between the learning progress and the auxiliary learning mode to obtain the current auxiliary learning mode for the target object; The voice output is for the current auxiliary learning mode of the target object for the user to confirm or reply.

[0008] In a possible implementation, after outputting the auxiliary learning content of the target auxiliary learning method to the target object, the method further includes: Within the second preset time period, in response to the user selecting and confirming the target object again, the auxiliary learning content of the target assisted learning method is output for the target object. When the target assisted learning method is object translation, the corresponding translation content is output in text. When the target assisted learning method is oral playback, the corresponding oral playback content is output in voice. When the target assisted learning method is oral practice, the oral practice reminder is output in voice.

[0009] In a possible implementation, the system further includes a user interface, wherein the user interface is used to display the online English article and allow a user to select and confirm the target object through the user interface; And / or, the system further comprises a target object selection confirmation module, the target object selection confirmation module being used to select and confirm the target object; And / or, the system further comprises a voice interaction module, wherein the voice interaction module is used for inputting and outputting voice data; And / or, the system further includes a text output module, and the text output module is used for outputting and displaying text content.

[0010] In a possible implementation, when the target auxiliary learning mode is oral practice, after the oral practice reminder is output by voice, the method further includes: Acquiring oral practice content input by the user's voice, and performing practice evaluation on the oral practice content to obtain an evaluation result; Generating oral practice guidance opinions and specifications according to the evaluation results, wherein the oral practice guidance opinions and specifications include speech speed adjustment opinions and pronunciation adjustment opinions; The speech speed adjustment suggestions and pronunciation adjustment suggestions are output to the user in voice.

[0011] In a possible implementation, the first processor is a cloud processor, the second processor is a local processor, and the first processor and / or the second processor are used to execute the computer-readable instructions to implement the intelligent learning assistance method, including: Acquire the network status of the intelligent learning assistance system, wherein the network status includes a normal network connection status and an abnormal network connection status; When the network connection state of the intelligent learning assistance system is a normal network connection state, using the first processor to execute the computer-readable instructions to implement the intelligent learning assistance method; When the networking state of the intelligent learning assistance system is an abnormal network connection state, the second processor is used to execute the computer-readable instructions to implement the intelligent learning assistance method.

[0012] In a possible implementation, when the network connection state of the intelligent learning assistance system is an abnormal network connection state, after using the second processor to execute the computer-readable instructions to implement the intelligent learning assistance method, the method further includes: Accumulating the time duration for the second processor to execute the computer-readable instructions to obtain an accumulated time duration, and attempting to establish a network connection when the accumulated time duration is greater than or equal to a preset time duration; After attempting to establish a network connection, if the network connection is normal, the second processor is switched to the first processor to utilize the first processor to execute the computer-readable instructions to implement the intelligent learning assistance method.

[0013] In a possible implementation, when the network connection state of the intelligent learning assistance system is an abnormal network connection state, after using the second processor to execute the computer-readable instructions to implement the intelligent learning assistance method, the method further includes: After attempting to connect to the network, if the network connection is still abnormal, continue to use the second processor to execute the computer-readable instructions to implement the intelligent learning assistance method, and periodically repeat the process of attempting to connect to the network until the network connection returns to normal, and then switch the second processor to the first processor to use the first processor to execute the computer-readable instructions to implement the intelligent learning assistance method.

[0014] In a second aspect, an intelligent learning assistance method is also provided in an embodiment of the present application, and the method includes: In response to the user selecting and confirming a target object, the target object includes an English sentence, an English paragraph or an English word in the English text; Generating a current auxiliary learning mode for the target object according to the historical auxiliary learning data and outputting the current auxiliary learning mode in voice, wherein the current auxiliary learning mode includes one of object translation, spoken language playback, and spoken language practice, and the current auxiliary learning mode in voice output is used for the user to confirm or reply; Acquire voice response data of the user to the current auxiliary learning mode of the voice output, and generate a target auxiliary learning mode based on the analysis result of the voice response data; The auxiliary learning content of the target assisted learning method is output for the target object. When the target assisted learning method is object translation, the translation content for the target object is output in text. When the target assisted learning method is oral playback, the oral playback content for the target object is output in voice. When the target assisted learning method is oral practice, the oral practice reminder is output in voice.

[0015] Different from the prior art, the intelligent learning assistance system based on NLP and speech processing provided in the embodiment of the present application includes a memory and a processor, the processor includes a first processor and a second processor, the memory stores computer-readable instructions, the first processor and / or the second processor is used to execute the computer-readable instructions to implement an intelligent learning assistance method, the method first responds to the user's selection and confirmation of the target object; then generates a current auxiliary learning mode for the target object according to the historical auxiliary learning data and performs speech output on the current auxiliary learning mode, the current auxiliary learning mode includes object translation, oral playback, oral practice One, the current auxiliary learning mode of the voice output is used for the user to confirm or reply; then the user's voice response data to the current auxiliary learning mode of the voice output is obtained, and the target auxiliary learning mode is generated based on the analysis result of the voice response data; finally, the auxiliary learning content of the target auxiliary learning mode is output to the target object, when the target auxiliary learning mode is object translation, the text output is the translation content for the target object, when the target auxiliary learning mode is oral playback, the voice output is the oral playback content for the target object, when the target auxiliary learning mode is oral practice, the voice output is the oral practice reminder. In this way, the user does not need to input manually, but can quickly obtain the required learning content through voice interaction and simple interface operations (such as clicking or touching), which greatly simplifies the learning process, reduces the learning burden of the user in the learning process, and thus improves the learning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0017] Figure 1 This is a schematic diagram of the interface of an intelligent learning assistance system based on NLP and speech processing in some embodiments of the present application; Figure 2 This is a flowchart of an intelligent learning assistance method in some embodiments of the present application; Figure 3 This is a schematic diagram of the module structure of an intelligent learning assistance system based on NLP and speech processing in some embodiments of the present application.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0021] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0022] With the rapid development of information technology, the field of education is undergoing profound changes. The traditional education model is gradually shifting towards intelligence and personalization, among which intelligent learning assistance systems based on natural language processing (NLP) and speech processing have become a research hotspot. Such systems aim to provide users with a more efficient and convenient learning experience through advanced artificial intelligence technology.

[0023] In existing intelligent learning assistance systems, users often need to manually select the type of learning resources and learning methods. For example, when users encounter English words or sentences they do not understand, they may need to first enter the query content through the keyboard or touch screen. However, this manual selection method is not only cumbersome to operate, but may also lead to poor learning effects due to different personal preferences and current learning status of users.

[0024] like Figure 1As shown, in an embodiment of the present application, an intelligent learning assistance system based on NLP and voice processing includes a user interface 100, a target object selection confirmation module 200, a voice interaction module 300, and a text output module 400, wherein the user interface 100 is used to display learning content, such as displaying English articles or English texts, and allows the user to select and confirm the target object through the user interface 100. The target object may include English sentences, English paragraphs, or English words in the English text. The target object selection confirmation module 200 is used to confirm the selection of the target object. For example, when the user selects part of the content (target object) in the user interface 100, the target object selection confirmation module 200 is popped up, and the user only needs to click on the pop-up box to complete the confirmation to confirm that the target object is selected (such as Figure 1 The target content with a medium gray background is selected). The voice interaction module 300 is used for inputting and outputting voice data, so that the user can interact with the system by voice. The text output module 400 is used for outputting and displaying text content, such as displaying translation content. By combining NLP and voice processing technology, this system provides users with an intuitive, convenient and efficient learning assistance platform, enabling them to understand and master various learning content more easily.

[0025] like Figure 1-Figure 3 As shown, the following takes the intelligent learning assistance system based on NLP and speech processing to perform the intelligent learning assistance method as an example. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order from here. Figure 2 The method includes the following steps S100 to S400: Step S100, in response to the user selecting and confirming a target object, the target object includes an English sentence, an English paragraph or an English word in an English text; like Figure 1 As shown, in the embodiment of the present application, when the user selects the target object of the content to be learned ( Figure 1 The learning content with a medium gray background is selected), and an object confirmation box (target object selection confirmation module 200) is automatically popped up. After the user touches or clicks the object confirmation box with the mouse, the system senses the user's selection of the target object and confirms it.

[0026] Exemplarily, in an online English learning platform, the target object may include an English sentence, an English paragraph, or an English word in an English text.

[0027] Step S200, generating a current auxiliary learning mode for the target object according to the historical auxiliary learning data and outputting the current auxiliary learning mode in voice, wherein the current auxiliary learning mode includes one of object translation, spoken language playback, and spoken language practice, and the current auxiliary learning mode output in voice is used for the user to confirm or reply; After the user selects and confirms the target object (such as an English sentence, paragraph or word in an English text), the system will first generate a recommended auxiliary learning method for the current target object based on historical auxiliary learning data (which may include the user's learning habits, level, and previously selected learning auxiliary methods, etc.). The recommended method can be object translation (i.e. translating the target object into a user-specified language, such as Chinese), oral playback (i.e. reading the original English sentence of the target object), or oral practice (i.e. reminding the user to practice oral imitation or retelling).

[0028] In one embodiment, the historical assisted learning data includes the user's accumulated learning time, and the step S200: generates a current assisted learning method for the target object based on the historical assisted learning data and voice outputs the current assisted learning method, including: determining the user's learning progress based on the user's accumulated learning time; querying a relationship mapping table between learning progress and assisted learning method based on the user's learning progress to obtain the current assisted learning method for the target object; and voice outputting the current assisted learning method for the target object for the user to confirm or reply.

[0029] Specifically, the system will first check the user's historical auxiliary learning data, especially the key indicator of cumulative learning time. By analyzing the cumulative learning time, the system can roughly evaluate the user's learning progress, that is, the user's current learning stage or level. The system maintains a relationship mapping table between learning progress and auxiliary learning methods. The relationship mapping table recommends corresponding auxiliary learning methods based on the different learning progress of users. Once the user's learning progress is determined, the system will look up the corresponding auxiliary learning method in this relationship mapping table, and generate a suitable auxiliary learning method for the current target object. Finally, the system will convey the current auxiliary learning method for the target object to the user in the form of voice through the voice output module. The user can hear this suggestion and confirm or reply according to his or her actual situation and needs. If the user agrees with this suggestion, the system will provide auxiliary learning content in this way; if the user disagrees, the system can provide other options or let the user choose by himself or herself.

[0030] Step S300, obtaining voice response data of the user to the current auxiliary learning mode of the voice output, and generating a target auxiliary learning mode based on the analysis result of the voice response data; In an embodiment of the present application, after hearing the auxiliary learning method recommended by the system, the user can confirm or reply through voice to indicate whether they accept the suggestion or put forward other requirements. If the user responds (confirms or replies) to the auxiliary learning method recommended by the system within a predetermined time (such as 5S), the system will obtain the user's voice response data and use voice recognition technology to convert it into text or understand its intention. Based on the analysis results of the user's voice response, the system will ultimately determine a target auxiliary learning method, that is, the auxiliary learning method that the user actually wants to adopt.

[0031] In one embodiment, if the voice response data of the current auxiliary learning mode to the voice output of the user is not obtained within a first preset time period, the current auxiliary learning mode is set as the target auxiliary learning mode.

[0032] Specifically, if the system does not receive the user's voice response data for the current auxiliary learning mode of the voice output within a certain period of time (i.e., within the first preset time length), the system will take a default operation, that is, directly set the current auxiliary learning mode as the target auxiliary learning mode.

[0033] In the embodiment of the present application, the system will first generate and voice output a recommended current auxiliary learning method based on the user's learning progress and other relevant factors. The system will start timing and wait for the user to respond to the current auxiliary learning method of the voice output. During the first preset time, the system will continue to monitor the user's voice input to determine whether the user has responded to the current auxiliary learning method. If the system receives the user's voice response data during this time period, it will generate the final target auxiliary learning method based on this response (it may maintain the original recommendation or adjust it according to the user's requirements). If the system still does not receive the user's voice response data after the first preset time, it will be considered that the user has accepted the current auxiliary learning method by default. Therefore, the system will directly set this current auxiliary learning method as the target auxiliary learning method and prepare to output subsequent learning content in this way. Through such a design, the system can continue to provide learning assistance without clear feedback from the user, avoiding system stagnation or errors caused by the user's failure to respond. At the same time, this also provides users with a smoother and seamless learning experience.

[0034] Step S400, outputting auxiliary learning content of the target assisted learning method to the target object. When the target assisted learning method is object translation, the translation content for the target object is output in text. When the target assisted learning method is oral playback, the oral playback content for the target object is output in voice. When the target assisted learning method is oral practice, the oral practice reminder is output in voice.

[0035] In the embodiment of the present application, the system will output the corresponding auxiliary learning content to the target object according to the type of the target auxiliary learning method. Specifically, when the target auxiliary learning method is object translation, the system will display the translation content for the target object in the text output module 400. This translation content can be the English text of the target object translated into other languages ​​(such as Chinese) specified by the user, so that the user can understand its meaning. When the target auxiliary learning method is oral playback, the system will read or play the oral content for the target object through the voice interaction module 300. For example, it can include the original sentence reading of the target object, or in some situations, the spoken explanation or example of the target object. When the target auxiliary learning method is oral practice, the system will give a reminder for oral practice through the voice interaction module 300. This reminder can include asking users to read, repeat or imitate the oral content of the target object to help them improve their oral skills. At the same time, the system can provide some additional oral practice guidance or feedback to help users practice better.

[0036] In other embodiments, when the target assisted learning mode is oral practice, after the voice output of the oral practice reminder, the method also includes: obtaining the oral practice content input by the user's voice, and performing a practice evaluation on the oral practice content to obtain an evaluation result; generating oral practice guidance opinions and specifications based on the evaluation result, the oral practice guidance opinions and specifications include speech speed adjustment opinions and pronunciation adjustment opinions; and outputting the speech speed adjustment opinions and pronunciation adjustment opinions to the user's voice.

[0037] In the embodiment of the present application, when the target auxiliary learning mode is oral practice, the system will further obtain the user's oral practice content after the voice output oral practice reminder, and evaluate it, and then generate specific oral practice guidance and specifications. Specifically, the system will first voice output an oral practice reminder, telling the user that oral practice is needed now, and give some basic practice requirements or suggestions. Then, the system will wait for and obtain the oral practice content input by the user. Including the oral content of the user following, repeating or imitating the target object. Then the system will automatically or manually evaluate the oral practice content submitted by the user. This evaluation will consider multiple aspects, such as speaking speed, pronunciation, intonation, grammar, etc. Through evaluation, the system can obtain one or more evaluation results, which reflect the user's performance in oral practice and areas that need improvement. Then, based on the evaluation results, the system will generate specific oral practice guidance and specifications. For example, speech speed adjustment opinions (such as speeding up or slowing down the speech speed), pronunciation adjustment opinions (such as correcting incorrect pronunciation or emphasizing the correct pronunciation method), etc. These opinions and specifications are intended to help users identify their shortcomings in oral practice and provide specific directions and methods for improvement. Finally, the system will convey the opinions on speed adjustment and pronunciation adjustment to the user through the voice output module. These opinions are presented in natural language so that users can easily understand and accept them. This application system not only provides users with the opportunity to practice speaking, but also helps them identify their shortcomings through evaluation and guidance, and gives specific suggestions for improvement. This helps users achieve better results in oral practice and improve their oral skills. At the same time, this also reflects the system's in-depth participation and personalized support for the user's learning process.

[0038] In another embodiment, after outputting the auxiliary learning content of the target-assisted learning method to the target object, the method further includes: Within the second preset time period, in response to the user selecting and confirming the target object again, the auxiliary learning content of the target assisted learning method is output for the target object. When the target assisted learning method is object translation, the corresponding translation content is output in text. When the target assisted learning method is oral playback, the corresponding oral playback content is output in voice. When the target assisted learning method is oral practice, an oral practice reminder is output in voice.

[0039] In an embodiment of the present application, after the system outputs the auxiliary learning content of the target object in a target-assisted learning manner, it also adds a function: within a second preset duration, if the user selects and confirms the same target object again, the system will re-output the auxiliary learning content of the target object in the same target-assisted learning manner. Through such a design, the system provides users with a more flexible and convenient way of learning. If the user needs to view or practice the auxiliary learning content of the same target object again during the learning process, they only need to select and confirm the target object again within the second preset duration. This helps to reduce the system's computing processing capacity, improve learning efficiency, and enhance the user's learning experience.

[0040] An embodiment of the present application also provides an intelligent learning assistance system based on NLP and speech processing, including a memory 110 and a processor 120, wherein the memory 110 is used to store computer-readable instructions, and the processor 120 is used to call the computer-readable instructions to execute the intelligent learning assistance method as described above.

[0041] In one embodiment, the processor 120 includes a first processor and a second processor, the first processor is a cloud processor, i.e., a cloud-based processor, and the second processor is a local processor. The first processor and / or the second processor are used to execute the computer-readable instructions to implement the intelligent learning assistance method, including: obtaining the networking status of the intelligent learning assistance system, the networking status including a normal network connection status and an abnormal network connection status; when the networking status of the intelligent learning assistance system is a normal network connection status, using the first processor to execute the computer-readable instructions to implement the intelligent learning assistance method; when the networking status of the intelligent learning assistance system is an abnormal network connection status, using the second processor to execute the computer-readable instructions to implement the intelligent learning assistance method.

[0042] Specifically, in the embodiment of the present application, the intelligent learning assistance system includes two processors: a first processor (cloud processor) and a second processor (local processor). The first processor is mainly responsible for processing tasks that require network connection, such as obtaining data from the cloud, performing cloud computing, etc. The second processor is responsible for processing tasks that do not require network connection, and providing basic learning assistance functions when the network connection is abnormal. In actual use, the system will first check its network status to determine whether it is currently in a normal network connection state or an abnormal network connection state. If the network status of the system is normal, the system will preferentially use the first processor (cloud processor) to execute computer-readable instructions to implement an intelligent learning assistance method. For example, it may include obtaining the latest learning resources from the cloud, performing cloud computing to provide more accurate learning suggestions, interacting with cloud servers to obtain real-time feedback, etc. If the network status of the system is abnormal (such as network disconnection, unstable connection, etc.), the system will switch to the second processor (local processor) to execute computer-readable instructions to implement an intelligent learning assistance method. In this case, the system will provide a set of predefined learning resources, use local algorithms to calculate learning suggestions, or provide feedback based on local data. Although the functions of the second processor (local processor) are relatively limited, the system can still provide users with a certain degree of learning assistance to ensure the continuity and stability of learning. Through such a design, the intelligent learning assistance system can intelligently select the processor to perform tasks according to its own networking status, thereby ensuring that users can be provided with efficient and stable learning assistance in different network environments. This improves the usability and flexibility of the system and provides users with a more seamless learning experience.

[0043] In other embodiments, when the networking state of the intelligent learning assistance system is an abnormal network connection state, after using the second processor to execute the computer-readable instructions to implement the intelligent learning assistance method, it also includes: accumulating the time for the second processor to execute the computer-readable instructions to obtain a cumulative time, and when the cumulative time is greater than or equal to a preset time, attempting to connect to the network; after attempting to connect to the network, if the network connection is normal, switching the second processor to the first processor to use the first processor to execute the computer-readable instructions to implement the intelligent learning assistance method.

[0044] It can be understood that when the system is in an abnormal network connection state, the second processor (local processor) will be used to perform intelligent learning assistance tasks. On this basis, the system also adds an intelligent switching mechanism, which will try to re-establish the network connection according to the working time of the second processor, and switch back to the first processor (cloud processor) after the connection is successful.

[0045] Specifically, when the networking status of the intelligent learning assistance system is abnormal network connection, the system will use the second processor to execute computer-readable instructions to provide basic learning assistance functions. During the assisted learning process, the system will start to accumulate the duration of the second processor executing computer-readable instructions, that is, the cumulative duration. The system will check regularly or in real time whether the cumulative duration has reached the preset duration threshold. This preset duration can be set according to actual needs, such as determined according to the temperature rise of the second processor. If the cumulative duration is greater than or equal to the preset duration, the system will try to reconnect to the network, such as checking network settings, reconnecting WiFi or mobile data networks, etc. After attempting to connect to the network, the system will check whether the network connection has returned to normal. If the network connection has returned to normal, the system will intelligently switch the currently used second processor to the first processor. After the switch is completed, the system will use the first processor to execute computer-readable instructions to achieve more powerful and richer intelligent learning assistance functions. After attempting to connect to the network, if the network connection is still abnormal, continue to use the second processor to execute the computer-readable instructions to implement the intelligent learning assistance method, and periodically repeat the process of attempting to connect to the network until the network connection returns to normal, and then switch the second processor to the first processor to use the first processor to execute the computer-readable instructions to implement the intelligent learning assistance method.

[0046] In the embodiment of the present application, the processor 120 is used to provide computing and control capabilities to control the intelligent learning assistance system based on NLP and voice processing to perform corresponding tasks, for example, to control the intelligent learning assistance system based on NLP and voice processing to perform the intelligent learning assistance method in any of the above method embodiments, the method comprising: in response to the user selecting and confirming the target object, the target object includes an English sentence, English paragraph or English word in an English text; generating a current auxiliary learning mode for the target object according to historical auxiliary learning data and performing voice output on the current auxiliary learning mode, the current auxiliary learning mode includes object translation, oral playback, A type of oral practice, wherein the current auxiliary learning mode of the voice output is used for the user to confirm or reply; the user's voice response data to the current auxiliary learning mode of the voice output is obtained, and a target auxiliary learning mode is generated based on the analysis result of the voice response data; the auxiliary learning content of the target auxiliary learning mode is output to the target object, when the target auxiliary learning mode is object translation, the text output is the translation content for the target object, when the target auxiliary learning mode is oral playback, the voice output is the oral playback content for the target object, and when the target auxiliary learning mode is oral practice, the voice output is an oral practice reminder.

[0047] The processor 120 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The above-mentioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0048] The memory 110 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the intelligent learning assistance method in the embodiments of the present application. The processor 120 can implement the intelligent learning assistance method in any of the above method embodiments by running the non-transitory software programs, instructions and modules stored in the memory 110.

[0049] Specifically, the memory 110 may include a volatile memory (VM), such as a random access memory (RAM); the memory 110 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD) or other non-transitory solid-state storage device; the memory 110 may also include a combination of the above types of memory.

[0050] To sum up, the intelligent learning assistance system based on NLP and speech processing of the present application adopts the technical solution of any one of the above-mentioned intelligent learning assistance method embodiments. Therefore, it has at least the beneficial effects brought by the technical solution of the above-mentioned embodiments, which will not be repeated here one by one.

[0051] The embodiment of the present application also provides a computer-readable storage medium, such as a memory including a program code, and the program code can be executed by a processor to complete the intelligent learning assistance method in the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), a magnetic tape, a floppy disk, and an optical data storage device.

[0052] The present application also provides a computer program product, which includes one or more program codes, which are stored in a computer-readable storage medium. The processor of the intelligent learning assistance system based on NLP and speech processing reads the program code from the computer-readable storage medium, and the processor executes the program code to complete the steps of the intelligent learning assistance method provided in the above embodiment.

[0053] A person skilled in the art will appreciate that all or part of the steps for implementing the above embodiments may be accomplished by hardware or by hardware associated with a program code, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0054] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0055] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that each embodiment can be implemented by means of software plus a general hardware platform, or by hardware. It can be understood by those skilled in the art that all or part of the processes in the above embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0056] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. An intelligent learning assistance system based on NLP and speech processing, characterized in that: The invention comprises a memory and a processor, wherein the processor comprises a first processor and a second processor, wherein the memory stores computer-readable instructions, and the first processor and / or the second processor are used to execute the computer-readable instructions to implement an intelligent learning assistance method, wherein the method comprises: In response to the user selecting and confirming a target object, the target object includes an English sentence, an English paragraph or an English word in the English text; Generating a current auxiliary learning mode for the target object according to the historical auxiliary learning data and outputting the current auxiliary learning mode in voice, wherein the current auxiliary learning mode includes one of object translation, spoken language playback, and spoken language practice, and the current auxiliary learning mode in voice output is used for the user to confirm or reply; Acquire voice response data of the user to the current auxiliary learning mode of the voice output, and generate a target auxiliary learning mode based on the analysis result of the voice response data; The auxiliary learning content of the target assisted learning method is output for the target object. When the target assisted learning method is object translation, the translation content for the target object is output in text. When the target assisted learning method is oral playback, the oral playback content for the target object is output in voice. When the target assisted learning method is oral practice, the oral practice reminder is output in voice.

2. The intelligent learning assistance system based on NLP and speech processing as claimed in claim 1, characterized in that: The first processor and / or the second processor is configured to execute the computer-readable instructions to implement an intelligent learning assistance method, wherein the method further comprises: If no voice response data of the current auxiliary learning mode to the voice output of the user is obtained within a first preset time period, the current auxiliary learning mode is set as the target auxiliary learning mode.

3. The intelligent learning assistance system based on NLP and speech processing as claimed in claim 1, characterized in that: The historical auxiliary learning data includes the cumulative learning time of the user, and the generating of the current auxiliary learning mode for the target object according to the historical auxiliary learning data and the voice output of the current auxiliary learning mode include: Determine the user's learning progress based on the user's cumulative learning time; According to the user's learning progress, query the relationship mapping table between the learning progress and the auxiliary learning mode to obtain the current auxiliary learning mode for the target object; The voice output is for the current auxiliary learning mode of the target object for the user to confirm or reply.

4. The intelligent learning assistance system based on NLP and speech processing as claimed in claim 1, characterized in that: After outputting the auxiliary learning content of the target auxiliary learning method to the target object, the method further includes: Within the second preset time period, in response to the user selecting and confirming the target object again, the auxiliary learning content of the target assisted learning method is output for the target object. When the target assisted learning method is object translation, the corresponding translation content is output in text. When the target assisted learning method is oral playback, the corresponding oral playback content is output in voice. When the target assisted learning method is oral practice, an oral practice reminder is output in voice.

5. The intelligent learning assistance system based on NLP and speech processing as claimed in claim 1, characterized in that: The system further comprises a user interface, wherein the user interface is used to display online English articles and allow a user to select and confirm the target object through the user interface; And / or, the system further comprises a target object selection confirmation module, the target object selection confirmation module being used to select and confirm the target object; And / or, the system further comprises a voice interaction module, wherein the voice interaction module is used for inputting and outputting voice data; And / or, the system further includes a text output module, and the text output module is used for outputting and displaying text content.

6. The intelligent learning assistance system based on NLP and speech processing as claimed in claim 1, characterized in that: When the target auxiliary learning mode is oral practice, after the oral practice reminder is outputted by voice, the method further includes: Acquiring oral practice content input by the user's voice, and performing practice evaluation on the oral practice content to obtain an evaluation result; Generating oral practice guidance opinions and specifications according to the evaluation results, wherein the oral practice guidance opinions and specifications include speech speed adjustment opinions and pronunciation adjustment opinions; The speech speed adjustment suggestions and pronunciation adjustment suggestions are output to the user in voice.

7. The intelligent learning assistance system based on NLP and speech processing as claimed in claim 1, characterized in that: The first processor is a cloud processor, the second processor is a local processor, and the first processor and / or the second processor are used to execute the computer-readable instructions to implement the intelligent learning assistance method, including: Acquire the network status of the intelligent learning assistance system, wherein the network status includes a normal network connection status and an abnormal network connection status; When the network connection state of the intelligent learning assistance system is a normal network connection state, using the first processor to execute the computer-readable instructions to implement the intelligent learning assistance method; When the networking state of the intelligent learning assistance system is an abnormal network connection state, the second processor is used to execute the computer-readable instructions to implement the intelligent learning assistance method.

8. The intelligent learning assistance system based on NLP and speech processing as claimed in claim 7, characterized in that: When the network connection state of the intelligent learning assistance system is an abnormal network connection state, after the second processor executes the computer-readable instructions to implement the intelligent learning assistance method, the method further includes: Accumulating the time duration for the second processor to execute the computer-readable instructions to obtain an accumulated time duration, and attempting to establish a network connection when the accumulated time duration is greater than or equal to a preset time duration; After attempting to establish a network connection, if the network connection is normal, the second processor is switched to the first processor to utilize the first processor to execute the computer-readable instructions to implement the intelligent learning assistance method.

9. The intelligent learning assistance system based on NLP and speech processing as claimed in claim 8, characterized in that: When the network connection state of the intelligent learning assistance system is an abnormal network connection state, after the second processor executes the computer-readable instructions to implement the intelligent learning assistance method, the method further includes: After attempting to connect to the network, if the network connection is still abnormal, continue to use the second processor to execute the computer-readable instructions to implement the intelligent learning assistance method, and periodically repeat the process of attempting to connect to the network until the network connection returns to normal, and then switch the second processor to the first processor to use the first processor to execute the computer-readable instructions to implement the intelligent learning assistance method.

10. An intelligent learning assistance method, characterized in that: The method comprises: In response to the user selecting and confirming a target object, the target object includes an English sentence, an English paragraph or an English word in the English text; Generating a current auxiliary learning mode for the target object according to the historical auxiliary learning data and outputting the current auxiliary learning mode in voice, wherein the current auxiliary learning mode includes one of object translation, spoken language playback, and spoken language practice, and the current auxiliary learning mode in voice output is used for the user to confirm or reply; Acquire voice response data of the user to the current auxiliary learning mode of the voice output, and generate a target auxiliary learning mode based on the analysis result of the voice response data; The auxiliary learning content of the target assisted learning method is output for the target object. When the target assisted learning method is object translation, the translation content for the target object is output in text. When the target assisted learning method is oral playback, the oral playback content for the target object is output in voice. When the target assisted learning method is oral practice, the oral practice reminder is output in voice.

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